Electricity networks are undergoing wholesale changes both from the generation and the user (load) sides. Major challenges in this direction are envisaged to be the management of largely dynamic load levels, due to e.g., charging a large number of plugin electric vehicles (PEVs), and the duality between loads and supplies, for instance, when PEVs are used in the “vehicle to grid” mode to mitigate power shortage and system imbalances. Generation, historically aggregated into large power plants and far from the user, is gradually moving towards being located at the distribution level and based on renewable sources, that is, intrinsically intermittent. This will require enhanced flexibility of the grid and the ability to accommodate islanding and microgrids.
The idea behind smart distributed grids and microgrids is to balance as much as possible locally between production and consumption. However, the deployment of intermittent renewable sources will inevitably lead to frequent imbalance between supply and demand, as exemplified by the difficulties in maintaining system balance due to wind power variability. Signal processing is certain to play a significant role in dealing with the complexity and uncertainty associated with the smart grid and in accurate real-time estimation of the system parameters, in both balanced and unbalanced conditions.
Unexpected frequency variations from the nominal value can trigger abnormal power system conditions that can propagate and aggregate within the power grid system. Some of the major abnormal power system conditions are discussed below:                Imbalance in the generation (G) and load (L). In the smart grid, the system will frequently switch between the main grid (MG) and microgrids (mG), with parts of the system completely switching off the MG for prolonged periods of time (islanding). The system frequency rises for G>L and decreases for G<L.        Single- and dual-phase faults. The system frequency is derived from the relationship between the three-phase voltages. Faults in one or two phases and voltage sags (sudden drop in voltage for a short period of time) will cause an incorrect frequency estimate and alarm spread through the system, although the actual system frequency was correct.        Dual character of load-supply. The smart grid employs dynamic loads and dual load-generator devices, such as PEVs, which can give the energy back to the grid in the case of emergency. Frequent switching will cause problems with reactive power, whose drifting causes oscillations of power levels and harmonics in frequency.        Harmonics. Some loads (power supplies, motors, heating elements) have nonlinear voltage to current characteristics and introduce harmonics, which may be slowly floating and not integer multiplies of system frequency. They may cause resonance in the system leading to significant increase in currents and overheating of transformers. Switching on the shunt capacitors for reactive power compensation also causes strong transients and harmonics that are damaging to some equipment.        Transient stability issues. Faults and short circuits could trigger instability, and actions such as shedding loads (or generators) that are needed to mitigate the problem require accurate frequency estimation.        
Accurate and fast frequency tracking is therefore a prerequisite to enable the system to respond quickly to such problems. Approaches to frequency estimation from a single phase in three-phase power networks result in non-unique solutions. Consequently, robust frequency estimators should consider all of the three-phase voltages. However, the processing required to determine the frequency from each of the three signals in a three-phase system is computationally expensive. Consequently, various processing algorithms have been proposed in order to attempt to reduce this computational complexity.
One such means for reducing computational complexity is the Clarke's αβ transform which produces a complex-valued signal from the three-phase voltages, where system frequency is obtained from the phase of this signal. Complex domain solutions for frequency tracking include phase-locked loops (PLLs), least squares, and demodulation methods. Recently, adaptive tracking algorithms based on the minimization of mean square error (MSE) have become a standard, as they are naturally suited to deal with noise, harmonics, and non-stationary environments. However, unbalanced events make it difficult to calculate phase angle, as in this case the complex-valued signal obtained from an unbalanced three-phase voltage source is represented as an orthogonal sum of positive (reflecting the energy transfer between generators and consumers) and negative (indicating imbalance between three-phase voltages) sequences. Standard complex linear adaptive filters can only cater for the positive sequences, whereas the negative sequences introduce a modelling error that oscillates at twice the system frequency. Hence, current systems for frequency tracking in three-phase systems are nowhere near optimal.